[1]丁洪金,宫法明.基于时序分析的人体活动状态识别与定位[J].计算机技术与发展,2019,29(04):82-86.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 017]
　DING Hong-jin,GONG Fa-ming.Human Activities Recognition and Location Based on Temporal Analysis[J].,2019,29(04):82-86.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 017]
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基于时序分析的人体活动状态识别与定位()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年04期
页码:
82-86
栏目:
智能、算法、系统工程
出版日期:
2019-04-10

文章信息/Info

Title:
Human Activities Recognition and Location Based on Temporal Analysis
文章编号:
1673-629X(2019)04-0082-05
作者:
丁洪金; 宫法明
中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580
Author(s):
DING Hong-jin; GONG Fa-ming
School of Computer & Communication Engineering,China University of Petroleum,Qingdao 266580,China
关键词:
目标检测; 动作分类; 深度学习; 特征提取; 时序行为
Keywords:
object detection; action classification; deep learning; feature extraction; temporal action
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 04. 017
摘要:
常规方法进行人体活动状态识别时,存在实时性差、实施困难,没有进行状态位置的时间定位等问题。针对这些问题,提出了基于时序分析的人体活动状态识别与定位方法。采用多层卷积神经网络对视频进行特征提取,采用时序动作性分组方法,产生不同精度的候选区域;采用基于结构化段网络的活动分类器对候选区域进行分类,以级联的方式进行端到端的训练,在未修剪长视频中识别并定位出站立、行走、跌倒及其时间节点。该方法不需要海量的手工标注样本进行训练,特征提取中包括时序特征的提取,相比于传统的活动状态分类方法不仅节约人力物力和增添了时间边界,还有效提高了检测的精度。在石油采油厂海上平台的监控视频的实验中证明了该方法的有效性和精确性。
Abstract:
There are many problems in the current methods of recognition of human activities such as poor implementation,bad real-time, no temporal location and so on. Thus,we propose a method for recognizing and locating human activities based on the temporal action recognition. Multi-layer convolutional neural network is used to extract video features,and sequential action grouping method is used to generate candidate regions with different accuracy. The activity classifier based on structured segment network is used to classify the candidate regions,and end-to-end training is conducted in a cascade manner. Standing,walking,falling and time nodes are identified and located in the untrimmed long video. The method does not require massive manual labeling of samples for training. Feature extraction includes the extraction of time features. Compared with the traditional method of action classification,it not only saves manpower and material resources,but also adds time boundaries,and effectively improves the accuracy of detection. Experiments in the offshore platform of the oil production plant prove the effectiveness and precision of the proposed method.

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更新日期/Last Update: 2019-04-10